Ensayo metabólico (Metabolitos en plasma)

CBM is a maintenance program that recommends maintenance actions based on the information collected through condition monitoring and forms a key component of the IVHM framework. CBM attempts to avoid unnecessary maintenance tasks by taking maintenance actions only when there is evidence of abnormal behaviours of a physical asset. A CBM program, if properly established and effectively implemented, can significantly reduce maintenance cost by reducing the number of unnecessary scheduled preventive maintenance operations, (Jardine et al., 2006).

A CBM program consists of three key steps, (Lee et al., 2004).

1. Data Acquisition step (information collecting), to obtain data relevant to system health.

2. Data Processing step (information handling), to handle and analyse the data or signals collected in step 1 for better understanding and interpretation of the data. 3. Maintenance Decision-Making step (decision-making), to recommend efficient maintenance policies.

3.3.1 Data Acquisition

The data acquisition step involves collecting and storing useful information about the asset that will enable CBM. There are two main classes of data that are collected, event based and condition based data. The event based data may contain information

Data Acquisition (Chapter 4) Data Processing (Chapters 5,6,7) Maintenance Decision Making

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that would be found in logs, e.g., when the machine/component was installed, breakdowns, overhauls, causes, and/or what was done to rectify the problem (e.g. preventative maintenance, lubricant changes, replacement parts, modifications etc.). Condition monitoring data is measurement based and relates to ascertaining the health condition/state of the physical asset.

Event data is usually entered into the system manually once a maintenance action has been completed, and Computerised Maintenance Management Systems (CMMS), (Davies, 2000), have been developed to handle the management of such data. The types of measurements that may be used to gather condition based monitoring data is based on largely sensor availability, the range of data that may be monitored is ever growing as sensor technology develops. As well as parameters such as vibration, acoustics, oil analysis, environmental, ultrasonic etc., new sensors are being developed to collect data such as vibration inside a gas turbine whilst in use (Kirianaki, 2002), (Senesky, 2009).

Wireless technologies, such as Bluetooth, have provided an alternative solution to cost-effective data communication. There may also be constraints to how condition based data is collected, the Civil Aviation Authority (CAA) for example, dictate that measurements must be non-invasive.

3.3.2 Data processing

The data that is collected will need to be cleaned. Data cleaning is important for both event based and conditioned based data. Data entered manually, which is usually the case with event based data, is often subject to errors as it involves a human element. Condition based data is subject to measurements from instrumentation and will incur a degree of measurement error; sensor faults and interference can also cause data error.

The next step of data processing is data analysis. A variety of models, algorithms and tools are available in the literature to analyse data for better understanding and interpretation of data. The models, algorithms and tools used for data analysis depend mainly on the types of data collected. (Jardine et al., 2006) state condition monitoring data collected from the data acquisition step are versatile. It falls into three categories: Value type: Data collected at a specific time epoch for a condition monitoring variable are a single value. For example, oil analysis data, temperature, pressure and humidity are all value type data.

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Waveform type: Data collected at a specific time epoch for a condition monitoring variable are a time series, which is often called time waveform. For example, vibration data and acoustic data are waveform type.

Multidimensional type: Data collected at a specific time epoch for a condition monitoring variable are multidimensional. The most common multidimensional data are image data such as infrared thermographs, X-ray images, visual images, etc. The way each type of data set is processed is the subject of many papers in literature, a variety of tools, algorithms and models are available to deal with the interpretation and extraction of features from each type of raw data. Waveforms and multidimensional data are subject to signal processing techniques.

Multidimensional data such as raw images are usually very complicated and immediate information for fault detection is unavailable. In these cases, image processing techniques are powerful tools to extract useful features from raw images for fault diagnosis (Nixon, 2002). Examples of where image processing techniques have been used for condition based monitoring and fault diagnosis are (Wang et al., 1993) and (Utsumi et al., 2001).

For waveform data analysis there are three main categories of analysis; time-domain, frequency-domain and time-frequency domain analysis.

Time-frequency domain methods are based upon the time waveform itself and include analysing the waveform in term of its descriptive statistics. Mean, standard deviation, peak-to-peak interval, crest factor as well as higher order statistical characterisations such as root mean square, kurtosis and skewness.

Time series modelling is an approach that is based on fitting the waveform data to a parametric time series model. Popular models in literature are Auto Regressive (AR) and Auto Regressive Moving Average (ARMA). (Isermann, 2011), (Pham, 2010) & (Carden, 2004).

Frequency-domain analysis is based on the transformed signal in frequency domain. The advantage of frequency-domain analysis over time-domain analysis is its ability to easily identify and isolate certain frequency components of interest. Fourier transform spectrum analysis techniques by means of a Fast Fourier Transform (FFT) are used to look at either components of interests within a frequency band or the whole spectrum and extract features of interests. (Lu et al., 2009), (Hameed et al., 2009), (Nandi et al., 2005).

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The Cepstrum, defined as the power spectrum of the logarithm of the power spectrum has the capability to detect harmonics and sideband patterns in power spectrum. High- order spectrum, i.e., bi-spectrum or tri-spectrum, can provide more diagnostic information than power spectrum for non-Gaussian signals.

There are two main classes of approaches for power spectrum estimation, namely the non-parametric approaches that estimate the autocorrelation sequence of the signal and then applies a Fourier transform to the estimated autocorrelation sequence. Secondly, the parametric approach that build a parametric model for the signal and then estimate power spectrum based on the fitted model. Among them, AR spectrum (Dron, 1998), (Stack, 2004)and ARMA spectrum (Salami, 2001) based on AR model and ARMA model, respectively, are the two most commonly used parametric spectra in machinery fault diagnostics.

Time-frequency analysis may a have significant advantage if the signal being monitored is non-stationary, which is common in moving machinery faults. Time- frequency analysis has the ability to analyse waveforms in both the time and the frequency domain and has been developed for non-stationary signal investigation. Traditional time–frequency analysis uses time–frequency distributions, which represent the energy or power of waveform signals in two-dimensional functions of both time and frequency to better reveal fault patterns for more accurate diagnostics. Examples include spectrograms and Wigner–Ville distributions.

Wavelets present a time-frequency analysis transform, which differs to the time- frequency approach by using a time-scale representation of a signal. Wavelets have been applied across numerous condition monitoring applications (Peng, 2004), (Zhu, 2009) and (Watson et al., 2010).

Traditionally, reliability analysis was carried out by fitting the event data to a time between events by a probability distribution, then this distribution was used for further analysis. CBM gives additional data and it is beneficial to use both the event and condition monitoring data together. From this combined analysis mathematical models can be built that properly describes the underlying mechanism of a fault or failure. The models built based on both event and condition monitoring data is the basis for maintenance decision support—diagnostics and prognostics (Jardine et al., 2006).

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Models include time-dependent Proportional Hazards Models (PHM); a commonly used parametric baseline hazard function is the Weibull hazard function, which is the hazard function of the Weibull distribution.

The concept know as potential-to-functional failure interval (P-F) is used in (RCM), the P-F describes failure patterns in condition monitoring. The P-F, is the time between a potential failure (P), which is some indicator of condition and the actual functional failure (F). Although difficult to quantify in real applications, the P-F interval offers a useful metric in condition monitoring.

The Hidden Markov model (HMM), (Rabiner, 1989), (Elliot, 1995), is another appropriate model for analysing event and condition monitoring data together. An HMM consists of two stochastic processes: a Markov chain with finite number of states describing an underlying mechanism and an observation process depending on the hidden state. A discrete-time HMM is defined by

𝑿𝑘+1 = 𝑨𝑿𝑘+ 𝑉𝑘+1 (25)

𝒀_𝑘 = 𝑪𝑿_𝑘+ 𝑊_𝑘 (26)

where Xk and Yk denote the hidden process and the observation process, respectively, Vk and Wk are noise terms with martingale increments, and A and C are parameters.

Event data and condition monitoring data are used to train the HMM, i.e., to estimate model parameters. (Bunks et al., 2000) and (Lee et al., 2004)